OpenRaiser/Intern-Atlas huggingface dataset:
Base Station Diversity Propagation Measurements at 73 GHz Millimeter-Wave for 5G Coordinated Multipoint (CoMP) Analysis
Venue: arXiv cs.IT 2017
Year: 2017

This paper describes wideband (1 GHz) base station diversity and coordinated multipoint (CoMP)-style large-scale measurements at 73 GHz in an urban microcell open square scenario in downtown Brooklyn, New York on the NYU campus. The measurements consisted of ten random receiver locations at pedestrian level (1.4 meters) and ten random transmitter locations at lamppost level (4.0 meters) that provided 36 individual transmitter-receiver (TX-RX) combinations. For each of the 36 radio links, extensive directional measurements were made to give insights into small-cell base station diversity at millimeter-wave (mmWave) bands. High-gain steerable horn antennas with 7-degree and 15-degree half-power beamwidths (HPBW) were used at the transmitter (TX) and receiver (RX), respectively. For each TX-RX combination, the TX antenna was scanned over a 120-degree sector and the RX antenna was scanned over the entire azimuth plane at the strongest RX elevation plane and two other elevation planes on both sides of the strongest elevation angle, separated by the 15-degree HPBW. Directional and omnidirectional path loss models were derived and match well with the literature. Signal reception probabilities derived from the measurements for one to five base stations that served a single RX location show significant coverage improvement over all potential beamformed RX antenna pointing angles. CDFs for nearest neighbor and Best-N omnidirectional path loss and cell outage probabilities for directional antennas provide insights into coverage and interference for future mmWave small-cells that will exploit macro-diversity and CoMP.
Angular Accuracy of Steerable Feature Detectors
Venue: arXiv cs.IT 2017
Year: 2017

The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated in the literature, and their localization and detection properties are well known. In this paper, we study the complementary topic of local orientation estimation, which has not received similar attention. Simply stated, the problem that we address is the following: estimate the angle of rotation of a pattern with steerable filters centered at the same location, where the image is corrupted by colored isotropic Gaussian noise. For this problem, we use a statistical framework based on the Cramér-Rao lower bound (CRLB) that sets a fundamental limit on the accuracy of the corresponding class of estimators. We propose a scheme to measure the performance of estimators based on steerable filters (as a lower bound), while considering the connection to maximum likelihood estimation. Beyond the general results, we analyze the asymptotic behaviour of the lower bound in terms of the order of steerablility and propose an optimal subset of components that minimizes the bound. We define a mechanism for selecting optimal subspaces of the span of the detectors. These are characterized by the most relevant angular frequencies. Finally, we project our template to a basis of steerable functions and experimentally show that the prediction accuracy achieves the predicted CRLB. As an extension, we also consider steerable wavelet detectors.
Accelerating Energy Games Solvers on Modern Architectures
Venue: arXiv cs.DC 2017
Year: 2017

Quantitative games, where quantitative objectives are defined on weighted game arenas, provide natural tools for designing faithful models of embedded controllers. Instances of these games that recently gained interest are the so called Energy Games. The fast-known algorithm solves Energy Games in O(EVW) where W is the maximum weight. Starting from a sequential baseline implementation, we investigate the use of massively data computation capabilities supported by modern Graphics Processing Units to solve the `initial credit problem' for Energy Games. We present four different parallel implementations on multi-core CPU and GPU systems. Our solution outperforms the baseline implementation by up to 36x speedup and obtains a faster convergence time on real-world graphs.
An Introduction to Rocker: Docker Containers for R
Venue: arXiv cs.SE 2017
Year: 2017

We describe the Rocker project, which provides a widely-used suite of Docker images with customized R environments for particular tasks. We discuss how this suite is organized, and how these tools can increase portability, scaling, reproducibility, and convenience of R users and developers.
Attacks on the Search-RLWE problem with small errors
Venue: arXiv cs.CR 2017
Year: 2017

The Ring Learning-With-Errors (RLWE) problem shows great promise for post-quantum cryptography and homomorphic encryption. We describe a new attack on the non-dual search RLWE problem with small error widths, using ring homomorphisms to finite fields and the chi-squared statistical test. In particular, we identify a "subfield vulnerability" (Section 5.2) and give a new attack which finds this vulnerability by mapping to a finite field extension and detecting non-uniformity with respect to the number of elements in the subfield. We use this attack to give examples of vulnerable RLWE instances in Galois number fields. We also extend the well-known search-to-decision reduction result to Galois fields with any unramified prime modulus q, regardless of the residue degree f of q, and we use this in our attacks. The time complexity of our attack is O(nq2f), where n is the degree of K and f is the residue degree of q in K. We also show an attack on the non-dual (resp. dual) RLWE problem with narrow error distributions in prime cyclotomic rings when the modulus is a ramified prime (resp. any integer). We demonstrate the attacks in practice by finding many vulnerable instances and successfully attacking them. We include the code for all attacks.
Confidence through Attention
Venue: arXiv cs.CL 2017
Year: 2017

Attention distributions of the generated translations are a useful bi-product of attention-based recurrent neural network translation models and can be treated as soft alignments between the input and output tokens. In this work, we use attention distributions as a confidence metric for output translations. We present two strategies of using the attention distributions: filtering out bad translations from a large back-translated corpus, and selecting the best translation in a hybrid setup of two different translation systems. While manual evaluation indicated only a weak correlation between our confidence score and human judgments, the use-cases showed improvements of up to 2.22 BLEU points for filtering and 0.99 points for hybrid translation, tested on English<->German and English<->Latvian translation.
End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies
Venue: arXiv cs.LG 2017
Year: 2017

Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer
Venue: arXiv cs.LG 2017
Year: 2017

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model the inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.
Decision support from financial disclosures with deep neural networks and transfer learning
Venue: arXiv cs.CL 2017
Year: 2017

Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long short-term memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives.
A batching and scheduling optimisation for a cutting work-center: Acta-Mobilier case study
Venue: arXiv cs.NE 2017
Year: 2017

The purpose of this study is to investigate an approach to group lots in batches and to schedule these batches on Acta-Mobilier cutting work-center while taking into account numerous constraints and objectives. The specific batching method was proposed to handle the Acta-Mobilier problem and a mathematical formalisation and genetic algorithm were proposed to deal with the scheduling problem. The proposed algorithm has been embedded in software to optimise production costs and emphasis the visual management on the production line. The application is currently being used in Acta-Mobilier plant and shows significant results
Abductive functional programming, a semantic approach
Venue: arXiv cs.PL 2017
Year: 2017

We propose a call-by-value lambda calculus extended with a new construct inspired by abductive inference and motivated by the programming idioms of machine learning. Although syntactically simple the abductive construct has a complex and subtle operational semantics which we express using a style based on the Geometry of Interaction. We show that the calculus is sound, in the sense that well typed programs terminate normally. We also give a visual implementation of the semantics which relies on additional garbage collection rules, which we also prove sound.
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
Venue: arXiv cs.RO 2017
Year: 2017

We introduce a real-time, constrained, nonlinear Model Predictive Control for the motion planning of legged robots. The proposed approach uses a constrained optimal control algorithm known as SLQ. We improve the efficiency of this algorithm by introducing a multi-processing scheme for estimating value function in its backward pass. This pass has been often calculated as a single process. This parallel SLQ algorithm can optimize longer time horizons without proportional increase in its computation time. Thus, our MPC algorithm can generate optimized trajectories for the next few phases of the motion within only a few milliseconds. This outperforms the state of the art by at least one order of magnitude. The performance of the approach is validated on a quadruped robot for generating dynamic gaits such as trotting.
Image retargeting via Beltrami representation
Venue: arXiv cs.CV 2017
Year: 2017

Image retargeting aims to resize an image to one with a prescribed aspect ratio. Simple scaling inevitably introduces unnatural geometric distortions on the important content of the image. In this paper, we propose a simple and yet effective method to resize an image, which preserves the geometry of the important content, using the Beltrami representation. Our algorithm allows users to interactively label content regions as well as line structures. Image resizing can then be achieved by warping the image by an orientation-preserving bijective warping map with controlled distortion. The warping map is represented by its Beltrami representation, which captures the local geometric distortion of the map. By carefully prescribing the values of the Beltrami representation, images with different complexity can be effectively resized. Our method does not require solving any optimization problems and tuning parameters throughout the process. This results in a simple and efficient algorithm to solve the image retargeting problem. Extensive experiments have been carried out, which demonstrate the efficacy of our proposed method.
Failures to be celebrated: an analysis of major pivots of software startups
Venue: arXiv cs.SE 2017
Year: 2017

In the context of software startups, project failure is embraced actively and considered crucial to obtain validated learning that can lead to pivots. A pivot is the strategic change of a business concept, product or the different elements of a business model. A better understanding is needed on different types of pivots and different factors that lead to failures and trigger pivots, for software entrepreneurial teams to make better decisions under chaotic and unpredictable environment. Due to the nascent nature of the topic, the existing research and knowledge on the pivots of software startups are very limited. In this study, we aimed at identifying the major types of pivots that software startups make during their startup processes, and highlighting the factors that fail software projects and trigger pivots. To achieve this, we conducted a case survey study based on the secondary data of the major pivots happened in 49 software startups. 10 pivot types and 14 triggering factors were identified. The findings show that customer need pivot is the most common among all pivot types. Together with customer segment pivot, they are common market related pivots. The major product related pivots are zoom-in and technology pivots. Several new pivot types were identified, including market zoom-in, complete and side project pivots. Our study also demonstrates that negative customer reaction and flawed business model are the most common factors that trigger pivots in software startups. Our study extends the research knowledge on software startup pivot types and pivot triggering factors. Meanwhile it provides practical knowledge to software startups, which they can utilize to guide their effective decisions on pivoting
About Microservices, Containers and their Underestimated Impact on Network Performance
Venue: arXiv cs.DC 2017
Year: 2017

Microservices are used to build complex applications composed of small, independent and highly decoupled processes. Recently, microservices are often mentioned in one breath with container technologies like Docker. That is why operating system virtualization experiences a renaissance in cloud computing. These approaches shall provide horizontally scalable, easily deployable systems and a high-performance alternative to hypervisors. Nevertheless, performance impacts of containers on top of hypervisors are hardly investigated. Furthermore, microservice frameworks often come along with software defined networks. This contribution presents benchmark results to quantify the impacts of container, software defined networking and encryption on network performance. Even containers, although postulated to be lightweight, show a noteworthy impact to network performance. These impacts can be minimized on several system layers. Some design recommendations for cloud deployed systems following the microservice architecture pattern are derived.
Local Radon Descriptors for Image Search
Venue: arXiv cs.CV 2017
Year: 2017

Radon transform and its inverse operation are important techniques in medical imaging tasks. Recently, there has been renewed interest in Radon transform for applications such as content-based medical image retrieval. However, all studies so far have used Radon transform as a global or quasi-global image descriptor by extracting projections of the whole image or large sub-images. This paper attempts to show that the dense sampling to generate the histogram of local Radon projections has a much higher discrimination capability than the global one. In this paper, we introduce Local Radon Descriptor (LRD) and apply it to the IRMA dataset, which contains 14,410 x-ray images as well as to the INRIA Holidays dataset with 1,990 images. Our results show significant improvement in retrieval performance by using LRD versus its global version. We also demonstrate that LRD can deliver results comparable to well-established descriptors like LBP and HOG.
Discrete Event, Continuous Time RNNs
Venue: arXiv cs.NE 2017
Year: 2017

We investigate recurrent neural network architectures for event-sequence processing. Event sequences, characterized by discrete observations stamped with continuous-valued times of occurrence, are challenging due to the potentially wide dynamic range of relevant time scales as well as interactions between time scales. We describe four forms of inductive bias that should benefit architectures for event sequences: temporal locality, position and scale homogeneity, and scale interdependence. We extend the popular gated recurrent unit (GRU) architecture to incorporate these biases via intrinsic temporal dynamics, obtaining a continuous-time GRU. The CT-GRU arises by interpreting the gates of a GRU as selecting a time scale of memory, and the CT-GRU generalizes the GRU by incorporating multiple time scales of memory and performing context-dependent selection of time scales for information storage and retrieval. Event time-stamps drive decay dynamics of the CT-GRU, whereas they serve as generic additional inputs to the GRU. Despite the very different manner in which the two models consider time, their performance on eleven data sets we examined is essentially identical. Our surprising results point both to the robustness of GRU and LSTM architectures for handling continuous time, and to the potency of incorporating continuous dynamics into neural architectures.
Development of computational models for emotional diary text analysis to support maternal care
Venue: arXiv cs.CY 2017
Year: 2017

We propose new computational models for analyzing self-reported emotional diary texts of pregnant women to support maternal care. We gathered affective ratings outside clinical setting and developed new models to facilitate interpretation and communication of affective expressions between persons representing different affective ratings. Relying on constructed emotion theory, models of dimensional emotion categories and affective ratings of Self Assessment Manikin, we demonstrate our new proposal to analyze linguistic data with computational models exploiting vector space and clustering methods. 35 persons having Finnish as a native language provided affective ratings for 195 emotional adjectives and 16 pregnancy-related nouns in Finnish in dimensions of pleasure, arousal and dominance. We developed new models to represent dependencies and differences of affective ratings between various population subgroup categorizations, including "women without children", "women with children" and "men without children" that we consider important population segments to be addressed in maternal care. Our affective ratings showed significant correlations between pleasure and dominance (like Warriner et al., 2013) and with previous data collections (Söderholm et al., 2013; Eilola & Havelka, 2010; Warriner et al., 2013). Our affective ratings had significant effects on categorizations based on gender, gender-parental role and the time of the day and duration of giving ratings. Our results indicate accordance with significant affectivity differences of gender and age (Warriner et al., 2013) and motherhood (Rosebrock et al., 2015). Our proposed models aim to support health-related communication. Our results suggest gathering next the affective ratings of patients of maternal care in a real clinical setting.
An Information Theoretic Framework for Active De-anonymization in Social Networks Based on Group Memberships
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, a new mathematical formulation for the problem of de-anonymizing social network users by actively querying their membership in social network groups is introduced. In this formulation, the attacker has access to a noisy observation of the group membership of each user in the social network. When an unidentified victim visits a malicious website, the attacker uses browser history sniffing to make queries regarding the victim's social media activity. Particularly, it can make polar queries regarding the victim's group memberships and the victim's identity. The attacker receives noisy responses to her queries. The goal is to de-anonymize the victim with the minimum number of queries. Starting with a rigorous mathematical model for this active de-anonymization problem, an upper bound on the attacker's expected query cost is derived, and new attack algorithms are proposed which achieve this bound. These algorithms vary in computational cost and performance. The results suggest that prior heuristic approaches to this problem provide sub-optimal solutions.
Pyroomacoustics: A Python package for audio room simulations and array processing algorithms
Venue: arXiv cs.SD 2017
Year: 2017

We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms. The content of the package can be divided into three main components: an intuitive Python object-oriented interface to quickly construct different simulation scenarios involving multiple sound sources and microphones in 2D and 3D rooms; a fast C implementation of the image source model for general polyhedral rooms to efficiently generate room impulse responses and simulate the propagation between sources and receivers; and finally, reference implementations of popular algorithms for beamforming, direction finding, and adaptive filtering. Together, they form a package with the potential to speed up the time to market of new algorithms by significantly reducing the implementation overhead in the performance evaluation step.